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Principle components analysis (PCA) can be used to detect repeating co-variant patterns of resting-state dynamic functional connectivity (DFC) of brain networks, accompanied with sliding-window technique. However, the robustness of PCA-based DFC-state extraction (DFC-PCA) is poorly studied. We investigated the reliability of PCA results and improved the robustness of DFC-PCA for a limited sample size. We first established how PCA-based DFC results varied with sample size and PC order in five rounds of bootstrapping with different sample s
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